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  3. Intermediate Data Visualization with ggplot2
  • 1

    Statistics

    Free

    A picture paints a thousand words, which is why R ggplot2 is such a powerful tool for graphical data analysis. In this chapter, you’ll progress from simply plotting data to applying a variety of statistical methods. These include a variety of linear models, descriptive and inferential statistics (mean, standard deviation and confidence intervals) and custom functions.

  • 2

    Coordinates

    The Coordinates layers offer specific and very useful tools for efficiently and accurately communicating data. Here we’ll look at the various ways of effectively using these layers, so you can clearly visualize lognormal datasets, variables with units, and periodic data.

  • 3

    Facets

    Facets let you split plots into multiple panes, each displaying subsets of the dataset. Here you'll learn how to wrap facets and arrange them in a grid, as well as providing custom labeling.

  • 4

    Best Practices

    Now that you have the technical skills to make great visualizations, it’s important that you make them as meaningful as possible. In this chapter, you’ll review three plot types that are commonly discouraged in the data viz community: heat maps, pie charts, and dynamite plots. You’ll learn the pitfalls with these plots and how to avoid making these mistakes yourself.

Initializing

Exercise

Pie charts

The coord_polar() function converts a planar x-y Cartesian plot to polar coordinates. This can be useful if you are producing pie charts.

We can imagine two forms for pie charts - the typical filled circle, or a colored ring.

Typical pie charts omit all of the non-data ink, which we saw in the themes chapter of the last course. Pie charts are not really better than stacked bar charts, but we'll come back to this point in the next chapter.

A bar plot using mtcars of the number of cylinders (as a factor), fcyl, is shown in the console.

Instructions 1/2

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  • 1
    • Run the code to see the stacked bar plot.
    • Add (+) a polar coordinate system, mapping the angle to the y variable by setting theta to "y".
  • 2
    • Reduce the width of the bars to 0.1.
    • Make it a ring plot by adding a continuous x scale with limits from 0.5 to 1.5.